We introduce a framework for in-context (zero-shot) inference/estimation of drift and diffusion functions underlying SDEs from empirical data of different dimensionalities.

We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework that enables generative diffusion bridge modeling with fractional noise for both paired and unpaired training data.

We propose a unified transformer framework to model mixed-type event sequences